Under the current computing constraints of limited qubits, quantum machine learning (QML) has been mainly deployed with quantum circuits [1]} for some classical image or audio processing problems. The existing QML models are often hybrid quantum-classical models that use quantum circuits as a parameterized encoder or decoder. Those quantum circuits project the input features into a high-dimension space and rotate the quantum state representations. Some preliminary studies on quantum circuit based convolution [2]} showed that hybrid models could work effectively for English spoken command recognition. However, the training time of a parameterized circuit usually relies on the required variational gradient approximation algorithms [3]}. Instead, a quantum kernel technique provides another perspective of using the characterises of quantum space on non-parametric learning, which could be beneficial to the few-shot and low-resource training scenarios.
